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Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

Martin Shkreli has been frequently called “the most hated man in America“. Aside from defrauding investors and being the envied owner of a one-of-a-kind Wu-Tang Clan album, the company of which he was chief executive, Turing Pharmaceuticals, purchased the sole US approved manufacturer of a toxoplasmosis treatment, pyrimethamine, and hiked its price from $13 to $750 per tablet. Price gouging is nothing new in the pharmaceutical sector. An episode of the recent Netflix documentary series Dirty Moneycovers the story of Valeant Pharmaceuticals whose entire business was structured around the purchase of drug companies, laying off any research staff, and then hiking the price as high as the market could bear (even if this included running their own pharmacies to buy products at these inflated prices). The structure of the US drug market often allows the formation of monopolies on off-patent, or generic, medication, since the process for regulatory approval for a new manufacturer can be long and expensive. There have been proposals though that this could be ameliorated by allowing manufacturers approved by other trusted agencies (such as the European Medicines Agencies) to sell generics in the US while the FDA approvals process takes place. The aim of this paper is to determine how many more manufacturers this would allow into the US drugs market. The authors identify all the off-patent drugs that have been approved by the FDA since 1939 and all the manufacturers of those drugs that were approved by the FDA and by other trusted agencies. No analysis is given of how this might affect drug prices, though there is a pretty obvious correlation between the number of manufacturers and drug prices shown elsewhere. The results show that the proposed policy would increase the number of manufacturers for a sizeable proportion of generics: for example, 39% of generic medications could reach four or more manufacturers when including those approved by non-FDA bodies.

The US healthcare system has long been an object of fascination for many health economists. It spends far more than any other nation on healthcare (approximately $9,000 per capita compared to, say, $4,000 for the UK) and yet population health ranks alongside middle-income countries like Cuba and Ecuador. Garber and Skinner wondered whether it was uniquely inefficient and identified or questioned a number of issues that may or may not explain the efficiency or lack thereof. One of these was the administrative burden of multiple insurance companies, which evidence suggests does not actually account for much of the total expenditure on health care. However, Garber and Skinner say this does not take into account time spent by clinical and non-clinical staff on administration within hospitals. In this opinion piece, Paul Sorum argues that internists should support a move to a single-payer system in the US. One of his four points is the administrative burden of dealing with insurance companies, which he cites as an astonishing 61 hours per week per physician (presumably spread across a number of staff). Certainly, this seems to be a key issue. But Sorum’s other three points don’t necessarily support a single-payer system. He also argues that the insurance system is leading to increasing deductibles and co-payments placed on patients, limiting access to medications, as drug prices rise. Indeed, Garber and Skinner note also that high deductibles limit the use of highly cost-effective measures and actually have the opposite effect of reducing productive efficiency. A single payer system per se would not solve this, it would need significant subsidies and regulation as well, and as our previous paper shows, other measures can be used to bring down drug prices. Sorum also argues that the US insurance system places an unnecessary burden from quality measures and assessment as well as electronic medical records used to collect information for billing purposes. But these issues of quality and electronic medical records have been discussed in the context of many health care systems, not least the NHS, as the political and regulatory framework still requires this. So a single-payer system is not a solution here. A key difference between the US and elsewhere that Garber and Skinner identify is that the US permits much more heterogeneity in access to and use of health care (e.g. overuse by the wealthy and underuse by the poor). Significant political barriers stand in the way of a single payer system, and since other means can be used to achieve universal coverage, such as the provisions in the Affordable Care Act, maybe internists would be better directing their energy at more achievable goals.

If you ever wondered whether the reason you didn’t get published in that top economics journal was that you didn’t know the right people, you may well be right! This article examines the social ties between authors and editors of the top four economics journals. Almost half of the papers published in these journals had at least one author with a connection to an editor, either through working in the same department, co-authoring a paper, or PhD supervision. The QJE appears to be the worst offender with (if I’ve read this correctly) all authors between 2000 and 2006 getting their PhD in either Harvard or MIT. So don’t bother trying to get published there! This article also shows that you’re more likely to get a paper into the journals when your former PhD supervisor is editing it. Given how much sway a paper published in these journals has on the future careers of young economists, it is disheartening to see the extent of nepotism in the publication process. Of course, one may argue that it just so happens that those that work at the top journals associate most frequently with those who write the best papers. But given even a little understanding of human nature, one would be inclined to discount this explanation. We have all previously asked ourselves, especially when writing a journal round-up, how this or that paper got into a particularly highly regarded journal, now we know…

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Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

I have an instinctive mistrust of buzzwords. They’re often used to avoid properly defining something, either because it’s too complicated or – worse – because it isn’t worth defining in the first place. For me, ‘real-world evidence’ falls foul. If your evidence isn’t from the real world, then it isn’t evidence at all. But I do like a good old ISPOR Task Force report, so let’s see where this takes us. Real-world evidence (RWE) and its sibling buzzword real-world data (RWD) relate to observational studies and other data not collected in an experimental setting. The purpose of this ISPOR task force (joint with the International Society for Pharmacoepidemiology) was to prepare some guidelines about the conduct of RWE/RWD studies, with a view to improving decision-makers’ confidence in them. Essentially, the hope is to try and create for RWE the kind of ecosystem that exists around RCTs, with procedures for study registration, protocols, and publication: a noble aim. The authors distinguish between 2 types of RWD: ‘Exploratory Treatment Effectiveness Studies’ and ‘Hypothesis Evaluating Treatment Effectiveness Studies’. The idea is that the latter test a priori hypotheses, and these are the focus of this report. Seven recommendations are presented: i) pre-specify the hypotheses, ii) publish a study protocol, iii) publish the study with reference to the protocol, iv) enable replication, v) test hypotheses on a separate dataset than the one used to generate the hypotheses, vi) publically address methodological criticisms, and vii) involve key stakeholders. Fair enough. But these are just good practices for research generally. It isn’t clear how they are in any way specific to RWE. Of course, that was always going to be the case. RWE-specific recommendations would be entirely contingent on whether or not one chose to define a study as using ‘real-world evidence’ (which you shouldn’t, because it’s meaningless). The authors are trying to fit a bag of square pegs into a hole of undefined shape. It isn’t clear to me why retrospective observational studies, prospective observational studies, registry studies, or analyses of routinely collected clinical data should all be treated the same, yet differently to randomised trials. Maybe someone can explain why I’m mistaken, but this report didn’t do it.

Obtaining health state utility values for children presents all sorts of interesting practical and theoretical problems, especially if we want to use them in decisions about trade-offs with adults. For this study, the researchers conducted a contingent valuation exercise to elicit children’s (aged 7-19) preferences for reduced risk of asthma attacks in terms of willingness to pay. The study was informed by two preceding studies that sought to identify the best way in which to present health risk and financial information to children. The participating children (n=370) completed questionnaires at school, which asked about socio-demographics, experience of asthma, risk behaviours and altruism. They were reminded (in child-friendly language) about the idea of opportunity cost, and to consider their own budget constraint. Baseline asthma attack risk and 3 risk-reduction scenarios were presented graphically. Two weeks later, the parents completed similar questionnaires. Only 9% of children were unwilling to pay for risk reduction, and most of those said that it was the mayor’s problem! In some senses, the children did a better job than their parents. The authors conducted 3 tests for ‘incorrect’ responses – 14% of adults failed at least one, while only 4% of children did so. Older children demonstrated better scope sensitivity. Of course, children’s willingness to pay was much lower in absolute terms than their parents’, because children have a much smaller budget. As a percentage of the budget, parents were – on average – willing to pay more than children. That seems reassuringly predictable. Boys and fathers were willing to pay more than girls and mothers. Having experience of frequent asthma attacks increased willingness to pay. Interestingly, teenagers were willing to pay less (as a proportion of their budget) than younger children… and so were the teenagers’ parents! Children’s willingness to pay was correlated with that of their own parent’s at the higher risk reductions but not the lowest. This study reports lots of interesting findings and opens up plenty of avenues for future research. But the take-home message is obvious. Kids are smart. We should spend more time asking them what they think.

Here we have a new journal that warrants a mention. The journal is sponsored by the International Society for Quality of Life Research (ISOQOL), making it a sister journal of Quality of Life Research. One of its Co-Editors-in-Chief is the venerable David Feeny, of HUI fame. They’ll be looking to publish research using PRO(M) data from trials or routine settings, studies of the determinants of PROs, qualitative studies in the development of PROs; anything PRO-related, really. This could be a good journal for more thorough reporting of PRO data that can get squeezed out of a study’s primary outcome paper. Also, “JPRO” is fun to say. The editors don’t mention that the journal is open access, but the website states that it is, so APCs at the ready. ISOQOL members get a discount.

We often hear that new drugs are expensive because they’re really expensive to develop. Then we hear about how much money pharmaceutical companies spend on marketing, and we baulk. The problem is, pharmaceutical companies aren’t forthcoming with their accounts, so researchers have to come up with more creative ways to estimate R&D spending. Previous studies have reported divergent estimates. Whether R&D costs ‘justify’ high prices remains an open question. For this study, the authors looked at public data from the US for 10 companies that had only one cancer drug approved by the FDA between 2007 and 2016. Not very representative, perhaps, but useful because it allows for the isolation of the development costs associated with a single drug reaching the market. The median time for drug development was 7.3 years. The most generous estimate of the mean cost of development came in at under a billion dollars; substantially less than some previous estimates. This looks like a bargain; the mean revenue for the 10 companies up to December 2016 was over $6.5 billion. This study may seem a bit back-of-the-envelope in nature. But that doesn’t mean it isn’t accurate. If anything, it begs more confidence than some previous studies because the methods are entirely transparent.

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Every Monday our authors provide a round-up of some of the most recently published peer reviewed articles from the field. We don’t cover everything, or even what’s most important – just a few papers that have interested the author. Visit our Resources page for links to more journals or follow the HealthEconBot. If you’d like to write one of our weekly journal round-ups, get in touch.

We start on a sombre note, with a paper that begs the question: why do we bother? A key purpose of health economic evaluation is to prevent the use of low-value, high-cost technologies. Influence on funding decisions is arguably a good basis on which to judge the impact of health economics. This study looks at funding decisions in England, Germany, the Netherlands and Sweden. The paper identifies key features of the HTA institutions and processes in each country. For all countries, there is very little evidence of economic evaluation having been the basis for the restriction of high-cost drugs. England found ways to support the funding of drugs for multiple sclerosis and cancer, despite high-cost and apparent low value. One positive impact might be in facilitating the negotiation of reduced prices – for example, through NICE’s patient access schemes. While the different countries have quite different processes, they have produced similar decisions in practice. The authors suggest that, despite having had limited impact on the outcome of funding decisions, health economics has influenced the process of decision making and the language of health care prioritisation. In this sense, health economics has value in rhetoric, increasing transparency and rational decision making. It’s an interesting idea that I’d like to see developed further, as the authors only provide a limited discussion of it. Personally, I think some distinction needs to be drawn between ‘health economics’ – as identified in the title – and ‘agency-mandated health technology assessment’. While many readers of this blog might do the former on a daily basis, I’d bet not many of us deal in the latter. I certainly don’t. So there’s a lot of ‘health economics’ that can’t – at least not directly – be judged on the basis of funding decisions. Yes, high-cost drugs backed by money-hungry Pharma evade HTA defences. But what about the other end of the spectrum? What about high-value interventions that have been commissioned because the economic evidence has been so compelling. Wishful thinking? Maybe not. Either way, we shouldn’t understate the value of health economics as rhetoric when dealing with capricious and myopic governments.

What do you mean you haven’t yet pre-ordered the new edition of the ‘Gold’ book from the famous Panel on Cost-Effectiveness in Health and Medicine? The original Panel was a big deal (not that I remember it, of course, as I was 8 years old), and so, presumably is the Second Panel. Maybe less so as relative consensus has developed in the use of health technology assessment in practice around the world. But we still need guidance. It’s ironic that the Panel was convened and funded by US organisations in a country that lags far behind in its use of economic evaluation in health technology assessment. This article in JAMA outlines the Panel’s recommendations. I can’t summarise them all here, so you probably need to go and read it all yourself. But know that there isn’t anything radical or unexpected. This Panel updated the original recommendations and created new ones where necessary. Threatening the validity of many a joke at economists’ expense, the Panel was able to reach consensus on all recommendations. Readers are chastised for not appropriately adopting a societal perspective as recommended by the first Panel, but then we are offered a compromise: “All studies should report a reference case analysis based on a health care sector perspective and another reference case analysis based on a societal perspective”. The Panel also recommend use of an “impact inventory”. This is a nice suggestion and I like the terminology. Including a disaggregated list of costs (and outcomes) improves transparency and makes studies more useful to future researchers. One new recommendation is that we should include unrelated future costs, which is something we saw discussed in a recent journal round-up. Another departure from the first Panel is that we are told to include productivity costs in the cost side of our equation. A suggestion that’s dropped in is that protocols should be written in advance of a study. I wish the panel had been more forceful with this one, as published protocols could go a long way in improving consistency, transparency and quality.

OK, I admit it: I went into this paper with a lot of scepticism. The QALY – that is, the combination of the quality and quantity of life – fundamentally makes sense. I’m not sure we need ‘an alternative’. The paper introduces some interesting ideas, but they aren’t as revolutionary as the author suggests and I’m not sure that it gets us anywhere. There are some problems from the outset. The article jumbles up positive and normative matters, criticising the QALY on the basis of its capacity to indicate what we might consider to be inequitable results. The author hints that the need for a new model derives from the QALY’s inappropriate combination of quality and duration of life. The most obvious criticism is that the constant proportional trade-off assumption does not hold. But then there’s no discussion of CPTO. The Load Model is presented as “radically different”, but it isn’t. Equations are shuffled so that we’re dealing in rates rather than time, but this adjustment appears to be inconsequential. It might be a more useful way to think about morbidity and mortality, but no argument to that end is presented. The main difference in the Load Model is that a ‘load’ is added for the negative impact of death (as opposed to being dead). Now, I have big problems with the way we handle ‘dead’ in health state valuation. I think it’s a more serious issue than we know (and we know quite a bit), so I am always glad to see attempts to fix it. Once you get past the superficial adjustments to the QALY, what’s really going on is that the Load Model is adding a third dimension to the valuation process; in addition to length of life and quality of life (in the Load Model it’s disease burden) we also have quality (or rather the burden) of death. But this could be incorporated into a QALY framework; I’ve spoken before about the notion of a 3- or otherwise multi-dimensional QALY. Given that death is so key to the distinction between the Load Model and the QALY, it’s unfortunate that in the worked example an entirely arbitrary value of questionable meaning is attributed to it. So the subsequent comparison between the two approaches seems meaningless. There may be more merit in the Load Model than I can see – perhaps I lack the immagination. But it seems to solve none of the problems associated with the QALY framework, while introducing new ones.

We’ve had quite a bit of discussion of 7 day services here on the blog. But the papers continue to flood in, much to the chagrin of Jeremy Hunt. This study doesn’t look at the most controversial case of extending hospital services, but investigates whether extended (evening and weekend) opening of GP practices reduces hospital attendance. The context is that providers in Manchester (England) were invited to bid for funding to roll out extended hours from December 2013. In total we’re looking at 56 practices who succeeded in the bid and 469 practices who provided services as normal. The analysis uses routinely collected hospital administrative data for almost 3 million patients from 2011 to 2014. A difference-in-differences OLS regression was used with propensity score matching to try and deal with the obvious selection problem. Of course, there was an increase in the number of GP visits: 33,519 in total. The main finding is that patients registered at practices with extended hours exhibited a 26.4% relative reduction in attendances for minor problems at A&E. So in this sense, extending opening hours seems to have satisfied its purpose. Though each emergency attendance ‘avoided’ corresponded to around 3 additional GP appointments. Unfortunately the study wasn’t able to determine the set-up and running costs of the extended GP services, so couldn’t carry out a proper cost-effectiveness analysis. And as we’ve discussed before in this context, that’s the question that really matters.